Provably scalable learning from quantum many-body data via locality
A scalable ML framework that efficiently learns from quantum many-body data by leveraging the exponential decay of correlations prevalent in noncritical systems is proposed, and it is rigorously proved that the GLQK substantially improves the sample complexity in the system size $n$ over the existing shadow kernel.
Koki Chinzei, Quoc-Hoan Tran, Norifumi Matsumoto et al.
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